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The Impact of Management Control on Autonomous
Motivation and Performance:
The Use of Control and the Role of Job Types
Niels Löbach
S4149491
Supervisor: Prof. dr. ir. P.M.G. van Veen-Dirks
Word count: 12.888
22-06-2020
Master Thesis
MSc Business Administration
Management Accounting and Control
Faculty of Economics and Business
University of Groningen
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ABSTRACT
Autonomous motivation has long been promoted as superior for an individual’s performance
and overall wellbeing. Although the feeling of pressure and control has shown to undermine
this type of motivation, psychology suggests that it can also be facilitated. However, how
management control can facilitate autonomous motivation and in turn drive organizational
success is still an open question. Moreover, the importance of the role of individuals’ job types
for their autonomous motivation is still under discussion. This thesis draws on Self-
Determination Theory and Job Characteristics Theory to investigate these relations and to
better understand the complex phenomenon of human motivation. In particular, the effects of
Simons’ levers-of-control (i.e. beliefs systems, boundary systems, diagnostic control systems
and interactive control systems) for two different job types (i.e. educational job type,
educational support job type) are examined using 215 employee surveys that were collected in
two Higher Educational Institutions in the Dutch public sector. The findings indicate that
beliefs systems have a positive impact on employees’ autonomous motivation. Further, the
examination of the Management Control System as a package revealed a positive effect of
positive controls (i.e. beliefs systems and interactive control systems) on autonomous
motivation. Furthermore, the study provides strong evidence that autonomous motivation
enhances performance. Lastly, findings do not show an effect of the job type on autonomous
motivation.
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CONTENT
I. INTRODUCTION ...................................................................................................................... 5
II. LITERATURE REVIEW ........................................................................................................... 7
2.1 Self-Determination Theory .................................................................................................................................. 7
2.1.1 Autonomous and controlled motivation .......................................................................................................... 7
2.1.2 Enhancing and undermining autonomous motivation .............................................................................. 8
2.2 Management Control........................................................................................................................................... 9
2.2.1 Management Control System ...................................................................................................................... 9
2.2.2 MCS as a package ........................................................................................................................................ 11
2.2.3 MC and motivation hypotheses .................................................................................................................. 11
2.3 The moderating effect of job types ..................................................................................................................... 13
2.3.1 Job Characteristic Theory ........................................................................................................................... 13
2.3.2 Hypothesis development ............................................................................................................................ 14
2.4 Motivation and performance ............................................................................................................................. 17
2.5 Conceptual model ............................................................................................................................................... 17
III. METHODS ........................................................................................................................... 18
3.1 Research method and sample.............................................................................................................................18
3.2 Measures .............................................................................................................................................................18
3.2.1 Independent and dependent variables .......................................................................................................18
3.2.2 Control variables ......................................................................................................................................... 19
3.3 Data analysis ....................................................................................................................................................... 21
3.3.1 Exploratory Factor Analysis and Reliability Analysis ................................................................................ 21
3.3.2 Hypothesis testing ...................................................................................................................................... 21
IV. FINDINGS ............................................................................................................................ 23
4.1 Descriptive statistics .......................................................................................................................................... 23
4.2 Early and late respondents................................................................................................................................ 24
4.3 Pearson correlation ........................................................................................................................................... 25
4.4 Hypothesis testing ............................................................................................................................................. 25
V. DISCUSSION AND CONCLUSION .......................................................................................... 27
REFERENCES ........................................................................................................................... 31
APPENDIX ................................................................................................................................ 36
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TABLES AND FIGURES
Figure 1. Self-Determination continuum ...................................................................................................................... 8
Figure 2. The job characteristic model ......................................................................................................................... 13
Figure 3. Conceptual model .......................................................................................................................................... 17
Table 1. Construct descriptions ................................................................................................................................... 20
Table 2. Descriptive statistics sample ......................................................................................................................... 23
Table 3. Early and late respondents ............................................................................................................................ 24
Table 4. Pearson correlation matrix ............................................................................................................................ 25
Table 5. Results of multiple linear regression analysis and moderation analysis ..................................................... 26
APPENDICES
Appendix A. Results of exploratory factor analysis for Levers of Control ................................................................. 36
Appendix B. Results of exploratory factor analysis for autonomous motivation ...................................................... 36
Appendix C. Results of exploratory factor analysis for autonomous motivation ...................................................... 36
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I. INTRODUCTION
The importance of autonomous motivation for individuals’ performance and overall well-
being has been advocated by psychology for decades (e.g. Ryan & Deci, 2000; Koestner &
Losier, 2002). In the last years management accounting researchers have begun to apply
theories from the field of psychology in the organizational context. Particularly, the public
sector has received increased attention (e.g. Van der Kolk et al., 2019) as employees’
motivation seem to differ from employees in private organizations. In addition, scholars have
recognized the importance of employee motivation for organizational performance in
universities (e.g. Sutton & Brown, 2016) and other higher educational institutions (HEIs) (e.g.
Zlate & Cucui, 2015; Hanaysha & Majid, 2018; Kuchava & Buchashvili, 2016). Since the
performance of both employees and managers directly influences students’ learning and their
overall experience at the HEI, it is people that propel the organization’s success. This vital role
of staff performance becomes even more evident when understanding that students are still
the main source of income in HEIs (Kuchava & Buchashvili, 2016).
Today, changing environments and the rising number of global competitors create new
challenges for HEIs. The increased competitive nature of higher educational systems causes
higher entry barriers and with that increases expectations from students and parents. In
addition, the government demands more economically efficient structures in HEIs
(Organisation for Economic Cooperation and Development, 2005). New public management
introduced business-like management styles and tools including management control systems
(MCSs) that should improve performance in the public sector (Ter Bogt & Scapens, 2012).
However, what works in the private sector might not necessarily lead to the same positive
results in the public sector. For instance, implemented MCSs in HEIs are often very outcome-
and performance-oriented, which can be problematic, when e.g. trying to evaluate
nonquantifiable variables such as the choice of the kind of research (Organisation for
Economic Cooperation and Development, 2005). In addition, management accounting
researchers have been concerned about the negative effects management control has on
employees in the public sector (e.g. de Bruijn, 2007; Parker et al., 2008). In fact, studies have
shown that an extensive use of performance measurement in universities can increase stress
and pressure (e.g. Tytherleigh et al., 2005; Woods, 2010). The main problem seems to be the
fact that the public sector is very different from the private sector as it has more complex
objectives (Frey et al., 2013), and employees are more intrinsically motivated (e.g. Georgellis
et al., 2011; Perry et al. 2010). Self-Determination Theory (STD) argues that self-motivated
behaviour should be encouraged to enhance individuals’ performance and overall well-being
(Deci & Ryan, 2000). Motivation that causes self-motivated behaviour is known as
autonomous motivation. Management accounting research has set its focus now more and
more on how different elements of the MCS affect different types of employee motivation
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including autonomous motivation (e.g. Groen et al., 2017, Van der Kolk et al., 2019). Within
this stream, several scholars have studied the effects of different control elements and MCSs
(e.g. Sutton & Brown, 2016, Van der Kolk et al., 2019). However, the puzzle of how the MCS
can promote autonomous motivation and thus, drive the organization’s performance in HEIs
is still not yet solved. Many researchers studied Simon’s ‘levers-of-control’ framework (e.g.
Granlund & Taipaleenmäki, 2005; Marginson, 2002; Mundy, 2010; Tuomela, 2005; Widener,
2007). Within this framework, there have been studies on the use of the MCS represented by
diagnostic and interactive control systems (Bobe & Taylor, 2010; Henri, 2006). The
framework presents two additional formal control elements - beliefs systems and boundary
systems – that have been widely neglected (Tessier & Otley, 2012). All levers of control
represent positive or negative forces that balance the amount of autonomy the MCS provides,
which in turn could also impact autonomous motivation. The dual role of those opposing
forces (i.e. positive and negative controls) in the MCS has received too little attention (Tessier
& Otley, 2012). These opposing forces reflect trade-offs “between freedom and constraint,
between empowerment and accountability, between top-down direction and bottom-up
creativity, between experimentation and efficiency” (Simons, 1994, p.4). Beliefs systems and
boundary systems also generate positive and negative forces and thus, could strengthen the
effect of interactive and diagnostic controls by creating more leverage. The study of the joint
use of controls that generate the same force might reveal new insights on combined effects of
the same force and of the MCS. Therefore, this study will build on past research to examine
the effect of the use of positive versus negative controls in the MCS on autonomous motivation
and in turn on performance.
In addition to sectorial differences public organizations can vary strongly in their job
types. Psychology researchers Hackman & Oldham (1975) have stressed the importance of job
characteristics for motivation. However, in management accounting research this field has
been mostly neglected. Buelens and Van der Broeck (2007) found contradicting results in
studies on motivation differences of public and private employees and argue that they can be
solely explained by the varying job types. Further, they see job content as a strong moderator
for motivation. The MCS directly impacts employees’ work environment and thus, provides
the conditions in which employees can be autonomously motivated. In addition, job types
differ in the way they satisfy the basic psychological needs that are necessary for autonomous
motivation. Lastly, different job types put different demands on the MCS, which could
determine how the MCS is perceived by individuals in different jobs. This thesis will explore
the effects of the MCS on autonomous motivation for two different job types: educational staff
and educational support staff. In particular, the aim is to answer the following research
question:
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RQ: What is the impact of the use of control and the job type on autonomous
motivation and in turn on performance?
The remainder of this thesis is structured as follows. First, in the literature review I will discuss
past research from the field of psychology and management accounting. This thesis will draw
on Self-Determination Theory and Job-Characteristic Theory to develop the hypotheses that
are presented at the end of Chapter II. This section ends with the illustration of the developed
hypotheses in the conceptual model. Subsequently, in Chapter III, the sample, the research
design and the data analysis will be presented. Chapter IV is to summarize the findings from
the data analyses, which are then discussed in the last chapter. In the fifth chapter, Discussion
and Conclusion, the findings are interpreted and discussed as well as the implications and
contributions to prior management accounting research as well as limitations of this thesis are
presented.
II. LITERATURE REVIEW
2.1 Self-Determination Theory
Self-Determination Theory (SDT) is a theory in the field of psychology that is concerned with
human motivation and behaviour. Central in SDT is to understand, what psychological factors
cause different types of motivation and how social structures can facilitate or hinder these
factors.
2.1.1 Autonomous and controlled motivation
People can be motivated in two different ways. Motivation can either derive from inner goals
or is somehow regulated externally (Ryan & Deci, 2000). Autonomous motivation means
“…engaging in a behaviour because it is perceived to be consistent with intrinsic goals or
outcomes and derives from the self, i.e. the behaviour is self-determined” (Hagger et al., 2014)
and can be both intrinsic and extrinsic. Intrinsic motivation as the most natural form of
autonomous motivation happens when people act, because they are interested in an activity
(Gagné & Deci, 2005, Ryan & Deci, 2000). Extrinsic motivation is more complex as it has both
autonomous and controlled forms. If a task is not interesting, people can be externally
incentivised or pressured to act, for instance through the use of rewards or punishments. In
those cases, their motivation is externally controlled.
However, people also carry out uninteresting tasks themselves and without pressure
because they know the task is important. This happens because of internalization, a process
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that describes the embodiment of values and external behavioural regulations and the
subsequent transformation into personal values. This process leads to self-determined
behaviour. Figure 1 illustrates SDT’s various forms of motivation, ranging from amotivation
to external motivation to introjected motivation to identified motivation to intrinsic
motivation. Internalization has different stages that reflect the degree of autonomous (versus
controlled) motivation. Identification refers to the integration of external regulations and
accepting them as their own personal values, i.e. it makes them part of who they are. In this,
people have a greater feeling of autonomy as their behaviour is consistent with their personal
values (Gagné & Deci, 2005). If this process is not fully successful because not all values are
internalized the motivation is still moderately controlled and called introjection. Introjection
involves a feeling of pressure that is caused by the regulation itself (Ryan, 1995). For instance,
people perform a task to avoid shame or guilt, or because it makes them feel worthy (i.e. they
involve their ego) (Deci & Ryan, 1985).
Figure 1. Self-Determination continuum. (Sheldon et al. 2003)
2.1.2 Enhancing and undermining autonomous motivation
Central to SDT is the question what psychological factors cause motivation. Researcher have
found that there are three basic psychological needs that are essential for motivation:
autonomy, competence and relatedness (Ryan & Deci, 2000). Deci & Ryan (2000) define
autonomy as “the individuals’ inherent desire to feel volitional and to experience a sense of
choice and psychological freedom when carrying out an activity” (Deci & Ryan, 2000) and
competence as the “individuals’ inherent desire to feel effective in interacting with the
environment” (Deci & Ryan, 2000). Lastly, relatedness can be defined as the “individuals’
inherent propensity to feel connected to others, that is, to be a member of a group, to love and
care and be loved and cared for” (Baumeister & Leary, 1995).
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Autonomy is crucial for autonomous motivation. Task choice can enhance the feeling of
autonomy. Contrarily, tangible rewards, surveillance, performance evaluations, imposed
goals, deadlines, threats and competitive pressure tend to reduce autonomy (Deci & Ryan,
1985; Ryan, 2000). This is because these factors can cause a shift in the perceived locus of
causality (PLOC) from internal to external (Ryan & Connell, 1989), i.e. when people feel
controlled, they stop being motivated by their interest, but rather by those external factors.
This undermining effect is also known as the crowding-out effect. Crowding-in, in contrast,
happens when external factors are perceived as supportive (or positively informative) and
enhance intrinsic motivation. (Frey, 2012). Moreover, people need to feel competent in their
behaviour to be autonomously motivated. Positive feedback and optimal challenging activities
have shown to support this feeling of competence. (Ryan, 2000).
The third need, relatedness, is important for extrinsic autonomous motivation. As
tasks are not interesting, people’s main reason for doing them, is that they mean something to
people they feel (or would like to feel) connected to. Within this, competence also plays a major
role. People tend to adopt activities that others value when they feel effective with regard to
those activities. Most importantly, internalization can only happen, when people feel
autonomous rather than controlled by rewards or punishment (Deci & Ryan, 2000).
Consequentially, in order to enhance autonomous motivation those three needs must be
facilitated.
2.2 Management Control
2.2.1 Management Control System
There is still no uniform definition for the term management control system. Some scholars
focus narrowly on employee behaviour (e.g. Merchant & Van der Stede, 2007), others leave
out the control aspect or the systematic use of management control elements (e.g. Chenhall,
2003). Malmi & Brown (2008) conclude that the most accurate definition is the following:
“management controls include all the devices and systems managers use to ensure that the
behaviours and decisions of their employees are consistent with the organization’s objectives
and strategies[…]If these are complete systems[…]then they should be called MCSs.” (Malmi
& Brown, 2008).
There are various MCS frameworks in the literature. However, Simons’ levers-of-control
framework has been widely accepted in research (e.g. Kruis et al., 2016; Widener, 2007; Henri,
2006). Central to the LOC framework is the question how organizations that demand
flexibility, innovation and creativity can use management control effectively for their strategy.
Simon (1995) argues that competing demands between creative innovation and predictable
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goal achievement need to be controlled as both have a great importance for the organization.
For this purpose, the levers-of-control framework provides four levers - beliefs systems,
boundary systems, diagnostic control systems and interactive control systems – that support
innovation and creativity while constraining employee behaviour to ensure predictable goal
achievement. These positive and negative forces allow managers to generate balance between
creativity and control (Simons, 1995).
Beliefs systems and boundary systems are both formal control systems. Beliefs systems as
positive controls are explicit sets of organizational definitions that communicate the
organization’s core values and mission (Simons, 1994). The primary purpose of beliefs systems
is to secure goal commitment and inspire and guide employees in their search for
opportunities and solutions (Simons, 1994).
Boundary systems form the counterpart to beliefs systems and play a negative role in
the MCS as they restrict the opportunity-seeking behaviour. This explicit set of organizational
definitions is typically expressed in negative terms or minimum standards, such as through
the code of conduct (Simons, 1995; Simons, 1994). Similar to beliefs systems, they are set to
guide employees by communicating activities that are off-limits. However, they also serve as
strategic boundaries for the search for innovative ideas and thereby prevent employees from
wasting resources (Simons, 1994). Simons (1995) compares beliefs systems to a racing car, of
which boundaries symbolize the brakes.
Diagnostic control systems represent the traditional use of the MCS as a feedback
system that compares actual performance to pre-set targets. Like a cockpit of an airplane,
managers use these information systems to monitor and analyse critical performance
variables and correct for deviations. These systems ensure the control for predictable goal
achievement. Diagnostic control systems are naturally negative as they restrict employees’
opportunity-search and experimentation to ensure the intended strategy is pursued (Simons,
1994, Simons, 1995).
In contrast, interactive control systems form a positive force that uses the MCS to
promote searching and learning. These formal systems enable a constant scanning of the
environment for emerging opportunities and risks (Simons, 1994; Simons, 1995) and serve to
share information on different organizational levels. Managers and subordinates are
encouraged to interactively discuss new arising information that concerns the organization’s
activities (Abernethy & Lillis, 1995; Speklé, 2001). This information can then be used by
managers to develop new strategies (Ahrens & Chapman, 2004).
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2.2.2 MCS as a package
The notion of studying the MCS as a collective (or package), rather than each element in
isolation, is a popular stream in the literature (see e.g. Widener, 2007; Malmi & Brown, 2008;
Mundy, 2010). MC elements do not operate as stand-alone functions but interrelate with each
other in a bigger system, the MCS (Malmi & Brown, 2008). Consequentially, if one element of
the system is changed, the nature of the entire system is changed, too. Research provided
strong evidence that systematic relationships between MC elements do exist in practice (e.g.
Widener, 2007). Therefore, it is necessary to study the impact on autonomous motivation
considering the MCS as a package.
2.2.3 MC and motivation hypotheses
Before hypothesizing the effects of the two forces on autonomous motivation, it is important
to discuss two issues first. There still seems to be ambiguity in the literature what good and
bad controls, enabling and coercive controls, and positive and negative controls are. Initially,
controls are neutral and set to enable a human behaviour that is congruent with the
organizations’ goals. However, these controls do not necessarily work as intended by the
management, which makes them bad controls due to their coercive effect on employees. In
other words, there is a gap between what managers want to achieve with controls and what
they actually achieve (Tessier & Otley, 2012). This leads to the second issue: employee
perception. Drawing back on SDT the environment created by the MCS needs to fulfil three
needs – autonomy, competence, relatedness. However, whether or not these needs are fulfilled
depends on how the individual perceives the used MC elements.
Negative controls are naturally constraining, but can also provide guidance, stability, and
predictability. Boundary systems restrict employees’ autonomy through rules, credos and
other formal procedures. Stating the organization’s goals in negative terms can generate a
feeling of authority and create distance between employees and top management. Further,
boundaries are often tied to punishment and sanctions, which can cause anxiety and guilt. An
extensive use of boundary systems could therefore reduce the feeling of relatedness and
autonomy as employees feel being controlled by the organization, rather than being connected
to it. The diagnostic use of control as feedback systems that monitor outcome and compare it
to pre-set performance standards include budget and profit plans, project monitoring systems
or goals and objective systems (Simons, 1994). The use of these performance measurement
systems in HEI settings can be problematic for multiple reasons. First, public organizations
generally have more complex and ambiguous goals that cannot be easily translated into
appropriate performance measures (Speklé & Verbeeten, 2014). Second, a strict focus on these
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performance measures can cause people to lose sight of the strategy these measures try to
represent and cause behaviour that is oriented to the (imperfect) measures rather than to the
strategy (Choi et al., 2013). The risk that the used measures do not fully represent the strategy
is even more likely as goals become more complex. Third, HEI staff have particularly high
levels of intrinsic motivation and are less respondent to external rewards (e.g. Sutton & Brown,
2016). Consequentially, negative feedback created by e.g. performance evaluations would have
a comparably much greater impact on employee motivation than, for instance, monetary
rewards that reward positive behaviour. In fact, performance management systems have
shown to undermine intrinsic motivation in HEIs (Ter Bogt & Scapens, 2012). An extensive
use of diagnostic controls in combination with boundaries could strengthen the feeling of
authority and control, create more distance to top management and pressure employees. As a
result, employees would feel less autonomous and less related to the organization. Further,
the increased pressure might cause employees to focus solely on performance targets and lose
sight of the “big picture”, which would negatively affect their actual performance. Poor
performance could also damage the feeling of competence.
Positive controls provide freedom, guidance, and inspiration. Employees in HEIs are primarily
motivated intrinsically (Georgellis et al., 2011). Beliefs systems provide core values and
purpose of the organization. Those are communicated through mission statements, credos and
statements of purpose. An extensive use of beliefs systems can strengthen the employees’
feeling of being connected to their colleagues and students, being part of the organization and
thus, satisfy the need for relatedness. Interactive controls promote dialogue throughout the
organization to stimulate creative innovation. Subject to interactive control systems are face-
to-face meetings, in which data generated by the system is discussed by managers, colleagues
and subordinates to make action plans. This happens in a positive environment that
encourages people to share information. Another feature of interactive controls is the use of
positive feedback (Simons, 1994). Regular meetings and information sharing can give
employees the feeling of being involved and of their effectiveness in the organization in a
supportive and positive manner without restricting their autonomy. Positive feedback has
shown to satisfy the need for competence (Gagné & Deci, 2005). Consequentially, based on
SDT, interactive controls and an extensive use of beliefs systems are likely to be perceived as
need-supportive and in turn, foster autonomous motivation. Hence, I hypothesize:
H1: The increased use of positive controls relative to negative controls is positively
associated with autonomous motivation.
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2.3 The moderating effect of job types
2.3.1 Job Characteristic Theory
Hackman & Oldham argue that the key for employee motivation arises from the work itself.
The researchers found that tasks that are less stimulating can diminish motivation and
productivity, whereas diversified activities have an enhancing effect. Job characteristics
including task variety, task identity, task significance as well as autonomy and feedback from
the job determine whether the individual reaches the three critical psychological states -
experienced meaningfulness, experienced responsibility for outcomes, knowledge of the
results of the activities - that are necessary for high levels of internal work motivation,
performance and job satisfaction (e.g. Hackman & Oldham, 1975).
Figure 2. The job characteristic model adopted from Hackman and Oldham (1975)
Skill variety refers to the level to which the job requires a wide-ranging number of activities,
that involve various skills and talents. Task identity is “the degree to which the job requires
completion requires completion of a “whole” and identifiable piece of work; that is, doing a
job from beginning to end with a visible outcome.” (Hackman & Oldham, 1975). Task
significance describes the degree to which the job has an impact on lives and work of others
both inside and outside the organization (Hackman & Oldham, 1975). Hackman and Oldham
(1980) argue that people in jobs who have a significant effect on the physical or psychological
well-being of others are likely to experience greater meaningfulness in the work. Those three
task characteristics are necessary to allow the employee to understand if the outcome of his
work has a significant impact on others and experience the work as meaningful (Hackman &
Oldham, 1975).
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Autonomy refers here to the amount of freedom and independence employees have in
carrying out their work and is necessary to experience responsibility for the outcomes of the
work. A job with high autonomy strengthens employees’ feeling of being responsible for their
own efforts and decisions more than when being instructed by superiors (Hackman & Oldham,
1975).
Feedback is defined as “the degree to which carrying out the work activities required
by the job results in the individual obtaining direct and clear information about the
effectiveness of his or her performance” (Hackman & Oldham, 1975) and is crucial for the
knowledge of actual results of work activities. Consequentially, these three psychological states
determine intrinsic motivation and individual performance (Hackman & Oldham, 1975).
Recalling SDT, Hackman & Oldham seem to define the same necessary psychological
needs for motivation and performance with the important addition of the influence of job
characteristics. Internal work motivation can also be translated into autonomous motivation
as it arises from within the individual, i.e. it is self-determined. The experienced
meaningfulness is determined by the impact on others and refers to the same need as
relatedness. In Hackman & Oldham’s model the job defines the level of autonomy, which then
determine the experienced responsibility for outcomes. This is also enabled when the need for
autonomy is satisfied. Lastly, the knowledge of results of the work serve the same function as
the need for competence since individuals need to know what the result of their work to feel
competent and motivated to then alter their behaviour accordingly. Overall, SDT agrees that
job characteristics will enhance autonomous motivation (Gagné et al., 1997).
2.3.2 Hypothesis development
Job types are naturally different from each other in the way their job characteristics satisfy
work-related basic needs and in turn cause employees to have different levels of autonomous
motivation. Further, the MCS directly impacts individuals’ work environment as it sets the
level of behavioural freedom and the degree and systematic way in which competence-relevant
feedback and organizational definitions are provided. This environment can be either need-
supportive or need-constraining. Consequentially, both the job characteristics and the MCS
determine the level of autonomous motivation.
Job types can be distinguished from each other by their level of task uncertainty and
interdependence. Interdependence describes the degree to which individuals need to work
with one another to perform a task (Mohr, 1971; Thompson, 1967) and requires much
coordination (Perrow, 1986). Hence, tasks and procedures often follow a strict process or
routine, which does not allow much freedom and creativity (Amabile, 1998). Functions in
HEIs can be broadly separated into two job types. The first job type includes all functions that
15
support the educational process including administrative jobs, finance and accounting jobs
and management jobs and is generally characterized by more interdependence and less task
uncertainty. In contrast, the second job type involves also uncertain tasks and copes with
relatively new problems. Further, individuals perform tasks more independently. Relatively
new problems require creativity to solve them (Perrow, 1986), for which more
experimentation and flexibility are needed (Simons, 1994). In HEIs this job type is not only
represented by research work, but also by high-quality teaching (Smith et al., 2014).
Both educational staff and educational support staff perform complex tasks that require
various skills and talents. However, the educational job type could have relatively higher levels
of task significance and task identity. Creative tasks are often performed by single employees
or in small teams rather in a larger group. In addition, the outcome of those tasks can be easier
attributed to individuals, whereas tasks that support the educational process are often
performed to complete a larger and more complex task. Both enables educational employees
to better experience tasks as a “whole” and identifiable piece of work and more directly
understand their personal impact on others which determines the experienced
meaningfulness of their work and is more likely to satisfy the need for relatedness. Moreover,
the educational job type could get relatively more feedback from the job. Direct contact to
students and peers, students’ grades, and the reputation inside and outside the institution
provide direct and clear information about the effectiveness of their performance, which
enables employees to feel competent. Lastly, the educational job type provides naturally more
freedom and flexibility in how to carry out the work as employees often work independently
and tasks are less routinized. This could cause employees to generally feel more autonomous.
In sum, I argue that the educational job type is more likely to experience satisfaction of the all
three basic needs – autonomy, relatedness and competence – and in turn has higher levels of
autonomous motivation. Hence, I hypothesize:
H2: Individuals working in an educational job type are more autonomously
motivated than individuals working in an educational support job type.
How strongly the MCS impacts employees’ autonomous motivation could also differ
depending on the job type due to different perceptions of positive and negative controls.
Positive controls promote experimentation, creative thinking and allow much freedom and
flexibility. First, the positive effect of beliefs systems could be perceived as relatively more
supportive by individuals working in educational jobs. Since, educational staff are primarily
motivated by teaching and research tasks (McInnis, 1996, 2000; Lacy & Sheehan, 1997), they
generally show lower commitment to the organization (Winter & Sarros, 2002). Beliefs
16
systems could strengthen the feeling of being connected to the organization in addition to the
tasks itself and in turn enable the feeling of relatedness. Interactive controls stimulate the
search for new ideas and opportunities and encourage experimentation and information
sharing. Educational staff that cope with relatively more uncertain tasks are more likely to
appreciate this system as it can help them to find solutions to relatively new problems. On the
other hand, too much freedom can also erode predictability and cause role ambiguity, for
instance, by not fully committing to budgets (Marginson & Ogden, 2005). Particularly,
educational support staff that generally performs relatively more certain tasks that are
predictable and often routinized could feel less guided by the system and less effective in their
work, which would mitigate their feeling of competence.
Negative controls are characterized by restrictive boundaries and diagnostic controls.
Educational staff are used to work independently and with much freedom. Formalised rules,
policies and procedures are likely to be perceived as strongly autonomy-restricting as they take
away flexibility of their work. In contrast, the educational support job type naturally offers less
freedom due to the high interdependence. Therefore, individuals working in those job types
are more likely to perceive boundaries as informational or guiding rather than as restrictive.
Diagnostic controls measure against pre-set standards using critical performance variables or
key success factors and reward their achievement. These systems are usually designed top-
down and thus, often leave little room for participation of employees that are most affected by
these systems including educational staff. Structures that limit teachers’ participation in
decision making can be demotivating (Winter & Sarros, 2002). Overall, the emphasis on
diagnostic controls including deadlines, surveillance and performance measurement could
take away the independence, flexibility and perceived autonomy of the educational job type
and be perceived as externally controlling. Ter Bogt & Scapens (2012) found two performance
measurement systems in university settings that inhibited creativity and increased pressure,
anxiety and guilt. Accordingly, after measuring performance on the basis of the number of
publications, projects that were uncertain to be published in international journals were
avoided, which resulted in more replication of studies that were successful in the past. In this
case, employees did not follow the strategy of performing high quality research but would
purely focus on the performance variable with maximizing the number of publications.
Further the researchers found that the fear of having a bad course evaluation increased
significantly (Ter Bogt & Scapens, 2012). All of the above indicates that the impact of the MCS
on autonomous motivation is relatively stronger for educational staff than for educational
support staff. This leads to the following hypothesis:
17
H4 H1
H3
H2
H3: Autonomous motivation of individuals who work in an educational job type is
stronger affected by the use of control.
2.4 Motivation and performance
“Motivation produces.” (Ryan & Deci, 2000). A meta-analysis by Cerasoli et al. (2014)
provides strong evidence that all types of motivation enhance performance. Particularly,
autonomous motivation could lead to greater performance in HEI settings for several reasons.
First, autonomous motivation is essential for tasks that are interesting. Employees who show
great interest in an activity set themselves more challenging goals (Cerasoli & Ford, 2014), are
willing to put more effort in their work and are eager to learn new skills (Simons et al., 2004).
Second, autonomous motivation is beneficial when tasks are more complex (Grolnick & Ryan,
1987) or need discipline to complete them (Koestner & Losier, 2002). When goals and tasks
are internalized, and basic needs are satisfied, the individual reaches its full cognitive and
motivational potential which leads to better performance (Sheldon et al., 2003). Since
employees in HEIs often perform complex task and are generally more interested in their
work, increased autonomous motivation could lead to better performance. Hence, I
hypothesize:
H4: Autonomous motivation is positively associated with performance.
2.5 Conceptual model
Figure 3. Conceptual model
Performance
Job type
Autonomous motivation
Positive/ negative
controls
18
III. METHODS
3.1 Research method and sample
In order to examine the effects of the MCS on autonomous motivation and the effects on
performance this thesis uses a quantitative research approach. More precisely, the survey
method is used as it allows to investigate the multi-faceted and complex phenomena between
management control and human motivation that exist in nature, while keeping the necessary
level of standardization for quantitative research and theory testing. For collecting data about
individuals’ attitudes, beliefs and perceptions that motivate their behaviour the survey method
is an appropriate tool (Speklé & Widener, 2018).
The data used for this study was collected by the University of Groningen in 2017. The
university granted access to the database for the purpose of this thesis. The sample originates
from employees and managers of two higher professional educational organizations in the
Dutch public sector. Tessier & Otley (2012) argue that there is a gap between what
management wants to achieve and what employees perceive. This thesis is concerned with
employees’ perception of management control. Since managers are involved in designing the
MCS, their perception differs from the one of employees. Hence, data from respondents with
managerial functions was excluded from the analysis. The sample size of employee data has a
total of 215 respondents and is presented in the descriptive statistics in Table 2.
3.2 Measures
The survey items that measure management control, motivation and performance were
adopted from past studies.
3.2.1 Independent and dependent variables
Management control package. Management control was measured based on Simons Levers-
of-Control framework. Levers-of-control items were selected from Kruis et al. (2016) and
Bedford & Malmi (2015). Beliefs and boundaries were adopted from Kruis et al. (2016) were
measured with four items, respectively, on a 7-point Likert scale. Diagnostic control and
interactive control systems were selected from Bedford & Malmi (2015). Diagnostic controls
were measured through five items, that determine the use of accounting information in form
of a cybernetic cycle. The interactive use was measured with five items: (1) intensive use by top
management, (2) intensive use by operating managers, (3) face-to-face challenge and debate,
(4) focus on strategic uncertainties, and (5) non-invasive, facilitating and inspirational
involvement (Bedford & Malmi, 2015). Both diagnostic and interactive control items were
measured with 7 anchors. To examine combined effects of the two positive controls relative to
19
the amount of negative controls in the package I compute a new variable that measures the
positive-negative-control ratio (PNR) of the MC package. The PNR is calculated by dividing
the sum of both positive controls (BELIEFS+INTER) by the sum of the two-negative control
(BOUND+DIAGN). The PNR allows to measure the amount of positive controls versus
negative controls on a continuous scale.
Autonomous motivation. Motivation was measured based on the different types of
controlled and autonomous motivation proposed by SDT in Gagné & Deci (2005). The survey
items were adopted from Gagné et al. (2014) who use the Multidimensional at Work Scale to
measure intrinsic motivation as well as controlled and autonomous types of extrinsic
motivation. Autonomous motivation was measured with 6 items, three items for intrinsic and
three items for autonomous extrinsic motivation (Identification). All types of motivation were
measured on a 7-point Likert scale.
Performance. Performance was measured on a unit level. An organizational unit is “…a
more or less unified administrative entity within the larger organization in which the unit’s
manager has at least some degree of authority over the set of tasks and processes of the unit.”
(Speklé & Verbeeten, 2014). The five survey items were adopted from Speklé & Verbeeten
(2014) and were measured on a 7-point Likert scale. Accordingly, performance is represented
by the amount and accuracy of work, the number of innovations, improvements and new ideas,
the reputation for work excellence, the attainment of goals, efficiency, and morale of the
personnel.
Job type. The job type was determined by the main activities of the respondent. The
item offered two choices (educational work or educational support work). The variable was
then transformed into a dummy variable (EDUC_JOB) that indicates whether the employee
mainly performs educational work or educational support work.
3.2.2 Control variables
Control variables are included since they could strongly influence the dependent variable.
They must therefore be held constant to achieve reliable results. This thesis follows Spector
and Brannick’s (2011) notion of explicitly control for variables based on theory or evidence
rather than randomly including control variables. For this study, the following five control
variables were selected.
Age. Motivation can be significantly influenced the age of the respondent. For instance,
Inceoglu et al. (2012) found that older employees were more intrinsically motivated than
younger ones.
Type of contract. Research has shown that whether the contract is fixed or temporary
can influence employee motivation. For instance, Kinman et al. (1998) found that short-term
20
contracts can increase stress, which would negatively affect autonomous motivation. Hence, I
introduce type of contract as a dummy variable (Contract_type_dummy).
Employee agreement. Flexible working hours can positively influence employee
motivation and performance significantly (Ahmad et al., 2013). Therefore, employee
agreement was added as another dummy variable. The variable refers to the number of
working hours of the employee and has two characteristics: fulltime agreement and part-time
agreement (Agreement_type_dummy).
Educational background. The level of education could influence the level of employee
motivation and performance. For instance, Kahya (2007) found differences in task
performance depending on the respondent’s educational level.
Table 1. Construct descriptions
Construct α Description Measurement
Control variables
Age
- Age Age of the respondent in years.
Tenure - Organizational tenure Tenure of the respondent in the organization.
Contract_type_dummy - Fixed term contract Contract type of the respondent {1=fixed term;
0= short-term}
Agreement_type_dummy - Fulltime agreement Employee agreement of the respondent
{1=fulltime; 0=part-time}
Education_dummy - Educational background Educational background of the respondent
{1=higher education (Bachelor; Master);
0=lower education (Secondary education;
Secondary vocational education}
Independent and dependent variables
PNR -
Positive-Negative-Controls-
Ratio of the MCS
Extent of positive controls relative to negative
controls expressed by the quotient of
(BELIEFS+INTER) and (BOUND+DIAGN).
BELIEFS .871 Beliefs systems Communication of organization's core values
based in Kruis et al. (2016).
BOUND .846 Boundary systems Communication of code of business conduct and
risks to be avoided based in Kruis et al. (2016).
DIAGN .965 Diagnostic control systems Use of accounting as part of a cybernetic control
cycle based on Bedford & Malmi (2015).
INTER .932 Interactive control systems Use of accounting information interactively
based on Bedford & Malmi (2015).
AUTON_MOT .823 Autonomous motivation Mean score of intrinsic motivation and extrinsic
autonomous motivation (identification) items
based on Self- Determination Theory and
adopted from Motivation-at-Work-Scale by
Gagné et al. (2014)
PERF .721 Performance Perceived performance of the respondent
measured on a unit level.
EDUC_JOB - Educational job type Job type {1=Educational jon; 0=Educational
supporting job}
21
Tenure. A meta-analysis by Ng and Feldman (2010) found that organizational tenure
can have a negative influence on the job performance. Organizational tenure was therefore
introduced as another control variable.
3.3 Data analysis
3.3.1 Exploratory Factor Analysis and Reliability Analysis
The software IBM SPSS AMOS 26 is used to analyse the data. Before testing the hypotheses, I
check the construct validity. First, I perform exploratory factor analyses on multi-item scales
and retained measures following Yong & Pearce (2013). Accordingly, each measure must load
into the correct factor. Items that load into multiple factors (cross-loadings) or load into the
wrong factor are removed. When performing a factor analysis for all items including
performance items, interactive controls and diagnostic controls items load into the same factor
and are impossible to separate. When removing performance items from the analysis, they
load into two different constructs. Hence, I perform three individual factor analyses: one for
the four levers of control, one for autonomous motivation and one for performance. The final
factor analysis is shown in the Appendix. Subsequently, multi-item constructs are computed
using the mean score of the items. Next, I perform a reliability analysis for each construct to
ensure the measurement scales are reliable. Cronbach alphas are calculated using the mean of
all items of the construct. All multiple-item constructs have good reliabilities with Cronbach
alphas ranging from .721 to .965 and are shown in Table 1.
3.3.2 Hypothesis testing
To test the hypotheses, the data is analysed in four different linear regression models. To
justify the use of each model the following requirements are assessed. First, I perform a
Durbin-Watson test to check for autocorrelations between the residuals. Autocorrelations
would indicate that the standard errors of the respective regression coefficients and therefore
the confidence intervals are distorted. The Durbin-Watson index should be between 1 and 3,
and optimally close to 2. Second, I test the models for multicollinearity. All variance inflation
factors (ViFs) should be smaller than 5 and must be at least smaller than 10. In addition, the
largest value in the condition index must be smaller than 30. Third, I examine all standardized
residuals that are bigger than 2 or smaller than -2 and check for extremes that are significantly
bigger than 2 or smaller than -2. Those could indicate incorrect data entries. Fourth, I visually
examine the residuals for normal distribution. Last, to verify linearity and homoscedasticity, I
examine the scatter plot for visible trends that would indicate heteroscedasticity. All models
fulfil those requirements and can be used.
22
Model 1 is a multiple regression (1) to test for direct effects of each single lever of control
(BELIEFS, BOUND, DAIGN, INTER) on autonomous motivation (AUTON_MOT). In all
models I control for age (Age), tenure (Tenure), type of contract (Contract_dummy),
employee agreement (Agreement_dummy) and educational background
(Education_dummy).
(1) AUTON_MOT= β0 + β1 BELIEFS + β2 BOUND + β3 DIAGN + β4 INTER + β5 Age
+ β6 Tenure + β7 Contract_dummy + β8 Agreement_dummy + β9
Education_dummy + ε
In Model 2 a multiple regression (1) is performed test the effect of the use of positive relative
to negative controls used in the MCS reflected by the PNR and the job type (EDUC_JOB) on
autonomous motivation (AUTON_MOT) as hypothesized in H1 and H2.
(2) AUTON_MOT= β0 + β1 PNR + β2 EDUC_JOB + β3 Age + β4 Tenure + β5
Contract_dummy + β6 Agreement_dummy + β7 Education_dummy + ε
Model 3 is a moderated multiple regression model (3) to test the moderating effect of job type
on the relationship between the PNR of the MCS and autonomous motivation. Moderated
multiple regression measures the relationship between a dependent variable and an
independent variable depending on the level of another independent variable (the moderator).
The moderated relationship (interaction effect) is modelled by including a product term
(PNR_x_EDUC_JOB) as an additional independent variable (e.g. Hartmann & Moers, 1999).
Since H3 postulates a moderating effect of the job type on the relationship between the PNR
of the MCS and autonomous motivation as measured by the multiplicative term, H3 is tested
by examining the significance of the coefficient of the interaction term. As a result, if tests on
the moderated regression reject the null hypothesis that the interaction coefficient is zero or
negative, then the impact of PNR of the MCS on autonomous motivation is more positive for
the educational job type and the hypothesis H3 would be supported.
(3) AUTON_MOT= β0 + β1 PNR + +β2 EDUC_JOB + β3 PNR_x_EDUC_JOB + β4
Age + β5 Tenure + β6 Contract_dummy + β7 Agreement_dummy + β8
Education_dummy + ε
Model 4 tests the positive effect of autonomous motivation (AUTON_MOT) on performance
(PERF) as hypothesized in H2 in a multiple regression analysis (4).
23
(4) PERF= β0 + β1 AUTON_MOT + β2 Age + β3 Tenure + β4 Contract_ dummy + β5
Agreement_dummy + β6 Education_dummy + ε
IV. FINDINGS
4.1 Descriptive statistics
This section describes the findings about the general characteristics of the employee sample
of the two HEIs that are shown in Table 2. The age of the respondents is divided into five
groups. Almost half of the employees are older than 51 years, whereas only 4% are 30 years or
younger. In addition, more than 40% of the employees have worked in the organization for
more than 10 years. The respondents consist mainly of educational staff with 65%. Educational
Table 2. Descriptive statistics sample
Variable Frequency (N=215) Percentage
Age
20-30 years 8 4%
31-40 years 53 25%
41-50 years 55 26%
51-60 69 32%
61+ 30 14%
Gender
Female 113 53%
Male 102 47%
Tenure (organizational)
0-5 years 67 31%
6-10 years 58 27%
11-20 years 52 24%
21-30 years 28 13%
31-40 years 9 4%
41+ years 1 < 1%
Tenure (departmental)
0-5 years 88 41%
6-10 years 62 29%
11-20 years 44 20%
21-30 years 15 7%
31-40 years 5 2%
41+ years 1 < 1%
Education
Primary education 0 0%
Bachelor’s degree 61 28%
Master’s degree or higher 129 60%
Secondary education 4 2%
Secondary vocational education 21 10%
Job type
Educational staff 139 65%
Educational support staff 76 35%
24
support staff made the remaining 35%. However, almost all respondents are highly educated,
with 88% holding a bachelor’s degree or higher. The number of males and female employees
is relatively equal in both organizations.
4.2 Early and late respondents
The surveys were sent out to the first organization starting from November 28 to December
12, 2017. For the second organization the surveys were sent from October 17 to December 30,
2017. In total, 75 employees responded from the first organization and 140 from the second
organization. The response time of the respondents varied strongly. Early and late
respondents are therefore determined by the first (early respondents) and fourth quantile (late
respondents) of the response time. Table 3 shows the means and standard deviations of main
constructs for early and late respondents of both organizations. Early and late respondents are
analysed (1) to check if the two organizations show major differences in the means and
standard deviations and (2) to test for non-response bias as late respondents can be a proxy
for non-response. The means give first impressions about the representation of the lever in
the MCS and the average level of autonomous motivation and performance of the respondents.
Similar means of the four levers of control indicate that both organizations have a very
balanced MCS with a PNR of around 1. Further, all employees show relatively high levels of
autonomous motivation with means of 5.53 (First organization) and 5,65 (Second
organization) and moderate performance with means of 3.56 (First organization) and 3.57
(Second organization). An independent 2-tailed t-test is performed for both organizations as
Table 3. Early and late respondents
First organization Second organization
Construct Total (n=75)
Early
respondents
(1st quantile;
n=19)
Late
respondents
(4th quantile;
n=19
Total (n=140)
Early
respondents
(1st quantile;
n=35)
Late
respondents
(4th quantile;
n=35)
Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
BELIEFS 3,77 1,19 3,59 1,16 4,04 1,12 3,66 1,16 3,68 1,37 3,59 1,30
BOUND 3,72 1,64 2,92 1,56 3,84 1,65 3,79 1,34 3,84 1,41 3,89 1,36
DIAGN 3,85 1,34 3,58 1,41 3,85 1,49 3,73 1,25 3,45 1,27 4,04 0,91
INTER 3,28 1,35 2,97 1,15 3,67 1,32 3,19 1,29 3,24 1,17 3,45 1,13
AUTON_MOT 5,53 0,96 5,61 0,93 5,4 1,06 5,65 0,98 5,70 1,02 5,67 0,99
PERF 3,56 0,4 3,68 0,39 3,64 0,36 3,57 0,47 3,61 0,54 3,59 0,48
25
well as for early and late respondents of each organization. Results show that there are no
significant differences in the means of the constructs in the two organization samples. Both
samples can therefore be pooled. The second organization shows a significant difference in the
mean of DIAGN (t(61.564)=-2.254; p=.028), which indicates that non-response bias could
have been be an issue in the second organization and must be considered when interpreting
the results.
4.3 Pearson correlation
The Pearson correlation matrix presents mutual correlations between the constructs that are
used in the regression models as well as it can reveal multicollinearity issues between
independent variables. All correlations between the variables are shown in Table 4. Significant
correlations exist between the four levers of control. In addition, beliefs systems (BELIEFS)
correlates significantly with autonomous motivation (AUTON_MOT). This effect is further
tested in Model 1 (please see Table 5.). Performance (PERF) is the only variable that correlates
significantly with autonomous motivation. Several control variables (8-12) correlate
significantly with each other. Tenure and Age have the highest correlation coefficient (.593).
However, all significant correlations in the variables that were used as independent variables
are below the threshold .70 and should not cause multicollinearity problems in the regression
analyses.
4.4 Hypothesis testing
The results from the multiple regression analyses to test the relationship between the PNR of
the MCS and autonomous motivation are shown in Table 5. In Model 1 a multiple linear
regression analysis was run to predict autonomous motivation from beliefs (BELIEFS),
Table 4. Pearson correlation matrix
Construct (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
(1) BELIEFS 1
(2) BOUND .361** 1
(3) DIAGN .364** .438** 1
(4) INTER .555** .485** .692** 1
(5) AUTON_MOT .210** -0,009 0,041 0,060 1
(6) EDUC_JOB -.183** -.206** -0,100 -.190** 0,122 1
(7) PERF 0,120 0,003 -0,017 0,012 .272** -0,009 1
(8) Age 0,057 0,015 0,007 -0,076 0,044 .138* -0,083 1
(9) Tenure 0,078 0,099 0,124 0,009 -0,074 -0,045 -0,041 .593** 1
(10) Contract_dummy -0,094 -0,045 -0,068 -0,129 -0,056 0,047 -0,069 .229** .344** 1
(11) Agreement_dummy -0,042 -0,032 -0,059 -0,110 0,061 -0,056 .243** -0,017 0,024 0,094 1
(12) Education_dummy -0,040 -.212** -0,051 -0,098 0,099 .430** -.138* 0,008 -.148* -0,036 -0,042 1
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
26
boundaries (BOUND), diagnostic controls (DIAGN) and interactive controls (INTER). A
significant regression equation was found (F(9,205)=2.034, p<.05), with an R² of .082 (Adj.
R²=.042). BELIEFS had a positive effect on autonomous motivation that was significant
(p=.002). All other levers of control were not significant predictors of autonomous motivation.
In Model 2 a multiple regression was performed to examine whether the use of positive
controls, relative to negative controls represented by the PNR of the MCS, and the job type
(EDUC_JOB) could significantly predict employees’ autonomous motivation (AUTON_MOT)
as proposed in H1 and H2. A significant regression equation was found (F(7,207)=1.961,
p<.100), with an R² of .062 (Adj. R²=.030). The use of positive controls relative to negative
controls had a positive significant effect on autonomous motivation (p=.017). This combined
effect of the positive controls was higher than the direct effect of beliefs systems that was found
Table 5. Results of multiple linear regression analysis and moderation analysis
Model 1 Coefficient
estimate (Standard error)
Model 2 Coefficient
estimate (Standard error)
Model 3 Coefficient
estimate (Standard error)
Model 4 Coefficient
estimate (Standard error)
Hypothesis
Intercept 4.431***
(.469) 5.176***
(.401) 4.505 (.486)
3.189*** (.031)
BELIEFS .201** (.064)
BOUND -.038 (.026)
DIAGN .026
(.068)
INTER -.039 (.076)
PNR .427** (.178)
.579* (.339)
H1: supported
EDUC_JOB .192
(.145) .401
(.421) H2: not supported
PNR_x_EDUC -.210 (.398)
H3: not supported
AUTON_MOT .134*** (.031)
H4: supported
Control variables
Age .010
(.007) .010
(.007) .009
(.007) -.004 (.003)
Tenure -.014
(.009) -.010
(.009) -.010
(.009) .001
(.004)
Contract_dummy -.061
(.209) -.150
(.208) -.138 (.210)
-.098 (.096)
Agreement_dummy .141
(.125) .151
(.125) .150
(.125) .201*** (.057)
Education_dummy .208
(.198) .065
(.215) .048
(.218) -.213** (.090)
R² .082 .062 .063 .162
Adjusted R² .042 .030 .027 .138
F value 2.034** 1.961* 1.745* 6.690***
***, ** and * indicate that coefficients are statistically significant at the 1%, 5% and 10% level, respectively. Significance is based on two-sided testing.
27
in Model 1. The job type was not a significant predictor of autonomous motivation.
Consequentially, H1 is supported and H2 must be rejected.
Model 3 examines the moderating effect of the job type as proposed in H3 in a moderation
analysis. The dependent variable for the analysis is autonomous motivation (AUTON_MOT).
Predictor variable for the analysis is the PNR of the MCS and the job type (EDUC_JOB). The
moderator variable evaluated for the analysis is the educational job type (EDUC_JOB). The
results show that there is no significant interaction effect between the PNR of the MCS and the
job type. Hypothesis H3 must therefore be rejected.
Model 4
A multiple linear regression was performed to predict performance based on the degree of
autonomous motivation as postulated in H4. A significant regression equation was found
(F(6,208)=6.690, p<.001), with an R² of .162 (Adj. R²=.138). Autonomous motivation had a
positive effect on performance that was significant (p<.0001). This strongly supports
hypothesis H4. Further, results show a significant effect of the type of agreement on
performance. Accordingly, fulltime employees perform better than part-time employees
(p=.001). Finally, higher educated employees show a lower level of performance than lower
educated employees. (p=.019). In sum, performance was predicted by autonomous
motivation, type of agreement and educational level.
V. DISCUSSION AND CONCLUSION
Management accounting research has long acknowledged the importance of autonomous
motivation for employee performance and overall well-being in knowledge-intensive
organizations such as HEIs. However, it still remains an unsolved puzzle how management
control can support autonomous motivation and make use of the positive effects that
accompany this type of motivation. This thesis seeks to shed more light on these complex
relations by investigating theoretically and empirically how the opposing forces that are
reflected by positive and negative controls in the MCS are associated with autonomous
motivation, and how this type of motivation relates to performance. For this, I draw on Self-
Determination Theory to hypothesize these relations and thus, continue a current stream in
management accounting literature (e.g. Sutton & Brown, 2016; Ter Bogt & Scapens, 2012; Van
der Kolk et al., 2019). Moreover, I seek to expand the scope of studying employee motivation
by examining the role of the job type as both predictor of autonomous motivation and
moderator of the impact of management control on autonomous motivation using Job
Characteristic Theory. Although founders of SDT Gagné and Deci agree that job characteristics
28
impact individual’s motivation they point out three major differences. First, SDT expands the
narrow focus of job characteristics as predictor for employee motivation by considering
management style as major influential factor on autonomous motivation. Second, Job
Characteristic Theory does not contemplate the compromising role of controlled motivation
for autonomous motivation. Third, whereas the need strength that enhances motivation is
central to Job Characteristic Theory, SDT is more concerned with the satisfaction of different
needs that enable a specific type of motivation (Gagné & Deci, 2005). Since this study is only
concerned with autonomous motivation both theories do not conflict with each other and can
both be used to make hypotheses and interpretations. This thesis provides several significant
findings both expected, based on theory and past studies, as well as surprising or somewhat
unexpected.
First, the results show that the hypothesized relationship between management control and
autonomous motivation indeed exist. In particular, findings indicate that an increased use of
positive controls relative to negative controls in the MC package leads to more autonomous
motivation. This outcome confirms Simons’ (1994) proposition that positive controls serve as
a force to motivate, guide and provide freedom and are overall of supportive nature to the
individual. STD explains this positive effect on autonomous motivation with the satisfaction
of the three basic needs: autonomy, competence and relatedness. Another interesting finding
was that solely a formal control system had a direct impact on autonomous motivation. As
illustrated in the Model 1 (please see Table 5.) only beliefs systems had a positive effect on
autonomous motivation that was significant, whereas all other control levers did not correlate
with autonomous motivation directly. This complements a study in the Dutch public sector by
Van der Kolk et al. (2019), who found that the communication of core norms and values (i.e.
cultural controls) enhances intrinsic motivation. Different from a case study by Sutton &
Brown (2016), who report positive effects of diagnostic controls such as performance
evaluations on autonomous motivation of researchers in a university, I did not find a direct
effect of the diagnostic or interactive use of control on autonomous motivation. Furthermore,
there was no significant impact of boundaries, which indicates that boundaries were perceived
as neutral rather than autonomy restricting. The examination of the MCS as a package showed
an increase of autonomous motivation when more positive controls relative to negative
controls were used, that was higher than the direct effect of beliefs systems. This could be
explained by existing complementary effects of other MC elements within the system.
However, more analysis is needed to make assumptions about what had caused this increased
effect as I did not find a direct effect of interactive controls.
Second, I found no evidence that supports the in H2 postulated relationship between
the job type and autonomous motivation. A possible explanation could be that the separation
29
into two job types was chosen too broadly. For instance, one can imagine that teaching a first-
year bachelor’s course with many participants involves different levels of task uncertainty and
interdependence compared to teaching a small master’s course with only a few participants or
supervising the writing of a master thesis. A similar spectrum of those two characteristics can
be assumed in the variety of jobs that support the educational process and were examined as
one job type in this study. Adler and Chen (2011) propose a third job type, that reflects both
high levels of interdependence and task uncertainty. Although the hypothesis must be rejected
in this study, I suggest more research on different job types.
Third, other than expected I could not find a significant interaction effect of
management control and job type. The assumption that the MCS would have a stronger effect
on the autonomous motivation of the educational staff can therefore not be declared correct.
Instead, all employees perceived the MCS as equally need-supportive. Sheldon et al. (2003)
state correctly that the control-oriented mindset of individuals that demands more structure
and direction does not lead to individuals wanting to be more controlled and that all
individuals benefit equally from more autonomy. Main argument for the assumption that the
MCS affects the educational job type stronger than the educational support job type was the
increased need of behavioural freedom due to higher task uncertainty and independence,
which would be relatively easier constrained by an extensive use of negative controls and
would at the same time benefit more from positive controls. Two factors could explain why
this assumption was not supported. First, negative controls were not perceived as constraining
at all. I did not find direct effects of any control elements on autonomous motivation other
than beliefs systems which indicates that they were perceived as neutral. Second, there were
very little outliers of strictly positive and strictly negative MCSs in the data sample. Overall,
the MCS was perceived as very balanced by the employees indicating that both organizations
made use of all four control levers to the same extent (please see Table 3.). This limited the
effects of extreme uses of either of the opposing forces or single MC elements which could have
constrained an individual. In sum, I have to reject the H3 with the remark that in other
samples and settings results could have been different. Therefore, I suggest further research
with a larger data sample.
Fourth, this thesis provides strong evidence that autonomous motivation is positively
associated with performance and thus, substantiates past research on this topic (e.g. Van der
Kolk et al., 2019; Sutton & Brown, 2016). This finding does not only underline the importance
of having overall motivated employees in HEIs, but also confirms the significance of
autonomous motivation as one specific type of motivation that drives performance.
It is worth mentioning that additional findings emerged from this thesis, some of which
were unexpected. First, I found that employees who had a fulltime agreement showed higher
levels of performance compared to employees that worked part-time. A possible explanation
30
for this effect could be that fulltime employees are overall more involved with their jobs
compared to part-time employees as a meta-analysis by Thorsteinson (2003) confirms.
Second, results show that employees with a lower educational level (secondary degree) rated
their performance higher than those with a higher educational level (bachelor’s degree or
higher), which supports a study by Kahya (2007), who reports a negative effect of the level of
education on task performance. Finally, in my sample both performance and autonomous
motivation were not influenced by age, tenure, or type of contract. These findings stand in
contrast to the reviewed literature (Inceoglu et al., 2012; Ng and Feldman, 2010; Kinman et
al. 1998).
This thesis makes several contributions to management accounting research. Most
importantly is the positive effect of beliefs systems on autonomous motivation. Past research
was mostly concerned with the use of the MCS represented by interactive and diagnostic
controls but less with the role of beliefs and boundaries. The findings could stimulate more
future research on those two formal control systems. Further, the examination of the MCS as
a package contribute to past research on this topic. An important feature of this thesis is the
examination of employee responses that allowed the investigation of human perception of
management control. Scholars experimented with mixed surveys (e.g. Groen et al., 2017) to
capture different perceptions on different issues (i.e. management and employee perception).
For this thesis I followed Tessier and Otley’s (2012) recommendation to study employee
responses instead of management response due to different perceptions. Accordingly, this
thesis relied purely on employee data for management control, motivation and performance.
This also allowed to better compare these three variables with each other. Additionally, the
study of variables that were measured on an individual level such as motivation and
management control in combination with a variable that was measured on a unit level (i.e.
performance) add to these so-called ‘cross-level’ studies in prior research (e.g. Van der Kolk
et al., 2019).
Beyond the already mentioned limitations this thesis has several general limitations. First, the
relatively small sample size of 215 participants from only two organizations limits the
generalization of this study. Only two different MCSs were examined that were both very
balanced. In addition, the job types were not represented equally by the data set. Only 35% of
the participant worked in educational support jobs. Another possible limitation was non-
response bias. Significant differences in means of a main construct in the second organization
of early and late respondents could have affected the results. Lastly, although quantitative
research allows to investigate the complex phenomenon of human motivation and enhancing
generalization of the results, quantitative models still explain relatively little of the variance
31
and give limited insight in human experience attached to this phenomenon. Future research
could therefore investigate the relationships also in qualitative studies.
The aim of this thesis was to answer the question what the impact of the use of control on
autonomous motivation for different job types is. In addition, this thesis aimed to substantiate
the findings by providing evidence that autonomous motivation enhances performance. In
regard to the research question I hypothesised a positive relationship between positive relative
to negative controls and autonomous motivation (H1). Further, I hypothesized a relationship
between job type and autonomous motivation so that the educational staff is more
autonomously motivated than the educational support staff (H2). Furthermore, I assumed
that an interaction effect between job type and management control exists (H2). In particular,
I postulated a stronger perceived effect of the use of control on the educational staff (H2) than
on the educational support staff. The findings support only H1. However, hypothesis H2 and
H3 must be rejected. Lastly, the examination of the relationship between autonomous
motivation and performance concluded that autonomous motivation and performance are
positively associated, which supports H4. Prior research has focussed much attention on those
control elements that determine the use of the MCS (i.e. interactive and diagnostic control)
(e.g. Henri, 2006; Bobe & Taylor, 2010; Bisbe & Otley, 2004). Future research could continue
this stream by focussing more on formal control systems (i.e. beliefs and boundaries) and on
the opposing forces reflected by positive and negative controls. In addition, the comparison of
different job types and their effect on different types of motivation could be studied. In
particular, it would be interesting to study the effects on intrinsic and extrinsic motivation
separately.
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APPENDIX
Appendix A. Results of exploratory factor analysis for Levers of Control
Component
Diagnostic control systems
Beliefs systems Boundary systems Interactive control
systems
BELIEFS_1 0,061 0,805 0,050 0,215
BELIEFS_2 0,154 0,687 0,170 0,353
BELIEFS_3 0,174 0,878 0,112 0,064
BELIEFS_4 0,139 0,858 0,109 0,102
BOUND_1 0,192 0,092 0,830 0,105
BOUND_2 0,121 0,007 0,864 0,141
BOUND_3 0,196 0,376 0,586 0,268
BOUND_4 0,183 0,142 0,828 0,065
DIAGN_1 0,912 0,104 0,180 0,178
DIAGN_2 0,887 0,129 0,154 0,230
DIAGN_3 0,889 0,151 0,194 0,247
DIAGN_4 0,884 0,178 0,176 0,194
DIAGN_5 0,835 0,125 0,159 0,274
INTER_2 0,418 0,241 0,231 0,775
INTER_3 0,396 0,307 0,179 0,758
INTER_4 0,435 0,299 0,189 0,748
Appendix B. Results of exploratory factor analysis for autonomous motivation
Component
Intrinsic motivation Identified motivation
IDENT_M_1 -0,034 0,872
IDENT_M_2 0,202 0,882
IDENT_M_2 0,470 0,745
INTR_M_1 0,827 0,107
INTR_M_2 0,944 0,136
INTR_M_3 0,895 0,191
Appendix C. Results of exploratory factor analysis for autonomous motivation
Component
Performance
PERF_2 0,624
PERF_3 0,554
PERF_4 0,669
PERF_5 0,809
PERF_6 0,639
PERF_7 0,627
